Member role: PhD Admissions Committee
Matthias Hein is Bosch endowed Professor of Machine Learning and the coordinator of the international master program in machine learning at the University of Tübingen. He is member of the Excellence Cluster “Machine Learning: New Perspectives for Science” and the Tübingen AI Center. His main research interests are to make machine learning systems robust, safe and explainable and to provide theoretical foundations for machine learning, in particular deep learning. He serves regularly as area chair for ICML, NeurIPS or AISTATS and has been action editor for Journal of Machine Learning Research (JMLR) from 2013 to 2018. He is an ELLIS Fellow and has been awarded the German Pattern recognition award, an ERC Starting grant and several best paper awards (NeurIPS, COLT, ALT).
Abhinav Valada is a Full Professor (W3) at the University of Freiburg, where he directs the Robot Learning Lab. He is a member of the Department of Computer Science, the BrainLinks-BrainTools center, and a founding faculty of the ELLIS unit Freiburg. Abhinav is a DFG Emmy Noether AI Fellow, Scholar of the ELLIS Society, IEEE Senior Member, and Chair of the IEEE Robotics and Automation Society Technical Committee on Robot Learning.
He received his PhD (summa cum laude) working with Prof. Wolfram Burgard at the University of Freiburg in 2019, his MS in Robotics from Carnegie Mellon University in 2013, and his BTech. in Electronics and Instrumentation Engineering from VIT University in 2010. After his PhD, he worked as a Postdoctoral researcher and subsequently an Assistant Professor (W1) from 2020 to 2023. He co-founded and served as the Director of Operations of Platypus LLC from 2013 to 2015, a company developing autonomous robotic boats in Pittsburgh, and has previously worked at the National Robotics Engineering Center and the Field Robotics Center of Carnegie Mellon University from 2011 to 2014.
Abhinav’s research lies at the intersection of robotics, machine learning, and computer vision with a focus on tackling fundamental robot perception, state estimation, and planning problems to enable robots to operate reliably in complex and diverse domains. The overall goal of his research is to develop scalable lifelong robot learning systems that continuously learn multiple tasks from what they perceive and experience by interacting with the real world. For his research, he received the IEEE RAS Early Career Award in Robotics and Automation, IROS Toshio Fukuda Young Professional Award, NVIDIA Research Award, AutoSens Most Novel Research Award, among others. Many aspects of his research have been prominently featured in wider media such as the Discovery Channel, NBC News, Business Times, and The Economic Times.
Begüm Demir is currently a Full Professor and the founder head of the Remote Sensing Image Analysis (RSiM) group at the Faculty of Electrical Engineering and Computer Science, TU Berlin and the head of the Big Data Analytics for Earth Observation research group at the Berlin Institute for the Foundations of Learning and Data (BIFOLD). Her research activities lie at the intersection of machine learning, remote sensing and signal processing. Specifically, she performs research in the field of processing and analysis of large-scale Earth observation data acquired by airborne and satellite-borne systems. She was awarded by the prestigious ‘2018 Early Career Award’ by the IEEE Geoscience and Remote Sensing Society for her research contributions in machine learning for information retrieval in remote sensing. In 2018, she received a Starting Grant from the European Research Council (ERC) for her project “BigEarth: Accurate and Scalable Processing of Big Data in Earth Observation”. She is an IEEE Senior Member and Fellow of European Lab for Learning and Intelligent Systems (ELLIS).
Vera Demberg is a professor for Computer Science and Computational Linguistics at Saarland University and Max Planck Research Fellow at the MPI for Informatics in Saarbrücken. Her research focus is on the intersection of Natural Language Processing and (Computational) Psycholinguistics. She specifically works on reliable and controllable neural language generation, individual differences in discourse and pragmatic processing in humans, as well as multimodal language models. She currently holds an ERC Starting Grant “Individualized Interaction in Discourse” (IDDISC).
Her most impactful publication was published at Cognition in 2008 by Demberg and Keller; it demonstrates that psycholinguistic theories of human sentence processing can be evaluated on broad-coverage naturalistic reading time corpora using NLP methods. Previously, such theories had only been evaluated on experimental datasets.
Vera Demberg is currently a member-at-large of the ACL Executive Board. She has been elected as a member of the academy of sciences and literature Mainz.